Prospects of measuring Gamma-ray Burst Polarisation with the Daksha mission
Bibliographic record
Abstract
The proposed Daksha mission comprises of a pair of highly sensitive space telescopes for detecting and characterising high-energy transients such as electromagnetic counterparts of gravitational wave events and gamma-ray bursts (GRBs). Along with spectral and timing analysis, Daksha can also undertake polarisation studies of these transients, providing data crucial for understanding the source geometry and physical processes governing high-energy emission. Each Daksha satellite will have 340 pixelated Cadmium Zinc Telluride (CZT) detectors arranged in a quasi-hemispherical configuration without any field-of-view collimation (open detectors). These CZT detectors are good polarimeters in the energy range 100 -- 400 keV, and their ability to measure polarisation has been successfully demonstrated by the Cadmium Zinc Telluride Imager (CZTI) onboard AstroSat. Here we demonstrate the hard X-ray polarisation measurement capabilities of Daksha and estimate the polarisation measurement sensitivity (in terms of the Minimum Detectable Polarisation: MDP) using extensive simulations. We find that Daksha will have MDP of~$30\%$ for a fluence threshold of $10^{-4}$ erg cm$^2$ (in 10 -- 1000 keV). We estimate that with this sensitivity, if GRBs are highly polarised, Daksha can measure the polarisation of about five GRBs per year.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".